Cardiovascular disease (CVD) is the No. 1 killer in the United States. More than 60% of all myocardial infarctions are caused by rupture of a vulnerable plaque. A large number of victims of the disease, who are apparently healthy, die suddenly without prior symptoms. About 95 percent of sudden cardiac arrest victims die before reaching a hospital. About 250,000 people a year die of coronary artery disease (CAD) without being hospitalized.
However, the mechanisms causing plaque rupture responsible for a stroke or cardiac arrest are poorly understood, and available screening and diagnostic methods are insufficient to identify the victims before the event occurs. For example, current technology for diagnosis of applicable cardiovascular diseases (e.g., carotid plaque rupture, coronary plaque rupture and aneurysm rupture) generally lacks accurate and reliable computational mechanical analysis. Clinically available magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound medical image equipment do not have computational mechanical analysis and related predictive computational indices for physicians to use in their decision making process. Even if some of the equipment may have some measurement data derived from some computational models, those models are overly simplified. The state of the art clinical decision making process is still based on morphologies derived from medical images with experiences from medical practice. Some two-dimensional (2D) MRI-based models and three-dimensional (3D) structure-only or fluid-only models have been known in the art. However, they are not generally adequate for decision-making purposes. In those models, mechanical analysis is either ignored or performed based on deficient models.
Therefore, a need exists for developing models adequate for decision-making purposes.